Electric companies require reliable technology cost information to make sound investments to consistently deliver electricity to customers and achieve strategic sustainability goals. The importance of reliable technology cost information will likely only increase as major shifts in the electric supply mix continue with the global energy transition. Many organizations produce global technology and/or macroeconomic outlooks to help various actors, including electric companies, better understand potential future states of the world. However, these outlooks may not provide actionable insights and/or outputs. This research develops cost projection factors that resource planners can directly apply to their own cost data for natural gas combustion turbines (NGCT), natural gas combined-cycle (NGCC), solar photovoltaic (PV), and onshore wind generation technologies, as well as small modular reactors (SMR) and lithium ion (Li ion) battery energy storage systems (BESS).
Baseline cost projections for NGCT, NGCC, solar PV, and onshore wind technologies were developed using a model of endogenous technology learning. Technology learning rates evaluated and deemed informative in Endogenous Learning for Projecting Future Capital Costs – Evaluation and Implications for Electric Power Generation Technologies (3002019786) were applied to demand outlooks from the Energy Information Administration’s (EIA) reference, low economic growth, and high economic growth scenarios, as well as EPRI’s U.S. Regional Economy, Greenhouse Gas, and Energy Model (REGEN) 100% renewable portfolio standard and net-zero electric sector scenarios. Cost projection factors for a given technology, scenario, learning rate, and year were developed before being converted to annual factors showing the magnitude of cost in a given future year relative to 2022 costs. Baseline cost projections for Li ion BESS were obtained from EPRI Program 94 on Energy Storage and Distributed Generation and then converted to annual cost projection factors through 2030. Finally, literature and historical data on five eras/experiences of traditional nuclear technology were analyzed in collaboration with experts from EPRI Program 41.08.01 on Advanced Nuclear Technology to identify trends and various potential future cost trajectories for SMR. Annual cost projection factors were developed from 2030 (the assumed baseline year for SMR) through 2050 based on the assumption that SMR might take on a similar cost trajectory to traditional nuclear globally, in South Korea, or various eras in the USA.